TY - JOUR
T1 - PredicTAIL, a prediction method for indoor environmental quality in buildings undergoing deep energy renovation based on the TAIL rating scheme
AU - Wei, Wenjuan
AU - Wargocki, Pawel
AU - Ke, Yao
AU - Bailhache, Simon
AU - Diallo, Thierno
AU - Carré, Samuel
AU - Ducruet, Pascal
AU - Maria Sesana, Marta
AU - Salvalai, Graziano
AU - Espigares-Correa, Carlos
AU - Greslou, Olivier
AU - Zirngibl, Johann
AU - Mandin, Corinne
PY - 2022
Y1 - 2022
N2 - The recently developed TAIL rating scheme enables assessment of the changes in the indoor environmental quality (IEQ) associated with a building’s deep energy renovation (DER) and classification of the resulting quality levels of the thermal (T), acoustic (A), and luminous (visual) (L) environments and indoor air quality (I). Since the TAIL rating is primarily based on measurements, it cannot be determined prior to renovation operations to help design the IEQ. To fill this gap, the PredicTAIL method was developed in the present study to predict the changes in ten of the twelve TAIL parameters as a result of DER. These parameters are indoor air temperature, relative humidity, sound pressure level, daylight factor, illuminance, and concentrations of carbon dioxide, formaldehyde, benzene, radon, and PM2.5; no prediction is made for ventilation rate or mold. To examine the feasibility of the PredicTAIL method and the sensitivity of the existing models for quantifying changes in the TAIL parameters corresponding to different renovation strategies, simulations were performed in a hotel and an office building using TRNSYS, IDA ICE, ACOUBAT, MATHIS-QAI, and PHANIE. These modeling tools were first benchmarked against the TAIL parameters measured in the buildings before renovation. Once the agreement between measurements and modeling was considered acceptable, four pragmatic renovation scenarios were applied, and their impact on the IEQ parameters was quantitatively modeled. The simulations showed that the quality levels of the IEQ parameters were improved or unchanged for some parameters but degraded for other parameters after DER. The changes in the IEQ parameters and the TAIL rating depended on the renovation scenarios, suggesting that the PredicTAIL method is sufficiently sensitive to guide renovation design.
AB - The recently developed TAIL rating scheme enables assessment of the changes in the indoor environmental quality (IEQ) associated with a building’s deep energy renovation (DER) and classification of the resulting quality levels of the thermal (T), acoustic (A), and luminous (visual) (L) environments and indoor air quality (I). Since the TAIL rating is primarily based on measurements, it cannot be determined prior to renovation operations to help design the IEQ. To fill this gap, the PredicTAIL method was developed in the present study to predict the changes in ten of the twelve TAIL parameters as a result of DER. These parameters are indoor air temperature, relative humidity, sound pressure level, daylight factor, illuminance, and concentrations of carbon dioxide, formaldehyde, benzene, radon, and PM2.5; no prediction is made for ventilation rate or mold. To examine the feasibility of the PredicTAIL method and the sensitivity of the existing models for quantifying changes in the TAIL parameters corresponding to different renovation strategies, simulations were performed in a hotel and an office building using TRNSYS, IDA ICE, ACOUBAT, MATHIS-QAI, and PHANIE. These modeling tools were first benchmarked against the TAIL parameters measured in the buildings before renovation. Once the agreement between measurements and modeling was considered acceptable, four pragmatic renovation scenarios were applied, and their impact on the IEQ parameters was quantitatively modeled. The simulations showed that the quality levels of the IEQ parameters were improved or unchanged for some parameters but degraded for other parameters after DER. The changes in the IEQ parameters and the TAIL rating depended on the renovation scenarios, suggesting that the PredicTAIL method is sufficiently sensitive to guide renovation design.
KW - IEQ
KW - IAQ
KW - Temperature
KW - Modeling
KW - Renovation design
KW - Indicator
U2 - 10.1016/j.enbuild.2022.111839
DO - 10.1016/j.enbuild.2022.111839
M3 - Journal article
SN - 0378-7788
VL - 258
JO - Energy and Buildings
JF - Energy and Buildings
M1 - 111839
ER -